Duke University
Grant: $1,099,524 - National Institutes of Health - Sep. 25, 2009
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Award Description: This application is in response to RFA-OD-09-004, 'GO' grants program area: ARRA Medical Sequencing Discovery Projects. Despite clear evidence for the importance of genetics in susceptibility to epilepsy, only limited progress has been made in identifying the specific genes that influence risk. Most of this progress has resulted from positional cloning strategies applied to rare families with Mendelian inheritance. In the search for genes that influence risk for complex epilepsies, substantial attention has been directed to the identification of common variants, and several major genome-wide association studies (GWAS) are now underway. However, the few results that have emerged from these studies so far suggest that common variants account for little of the genetic component of the disorder. On the other hand, very recent studies have shown an increased frequency of rare and very large genomic deletions in people with epilepsy, suggesting that rare genetic variation may be important in this disease. If this is true, structural variants are unlikely to be the only types of pathogenic variation, and additional rare variants may be identified by sequencing. Thus in the current study, we propose to employ next generation sequencing to identify rare gene variants that contribute to epilepsy and are not represented, either directly or indirectly (through high linkage disequilibrium), on the sequencing platforms used in GWAS. Our strategy for this effort will focus on families containing multiple individuals with non-acquired (idiopathic or cryptogenic) epilepsy. The families to be studied have been previously collected and phenotyped in detail, and contain an average of 3.5 affected individuals with a range of different types of epilepsy. We will select one or more affected individuals from each family and use next generation sequencing approaches to identify most of the genetic variation present in each of the selected individuals. These variants will then be evaluated using bioinformatic criteria to identify those most likely to influence epilepsy. We will then test whether these candidate mutations cosegregate with epilepsy within the families, and whether they are associated with epilepsy in a much larger sample of unrelated patients and controls. For each identified variant with strong evidence for association (in families or unrelated individuals), we will examine whether the effect is specific to one or more clinically defined subgroups (e.g., focal or generalized epilepsy), or whether it appears to raise risk more generally.
Project Description: See Award Description
Jobs Summary: N/A (Total jobs reported: 0)
Project Status: Not Started
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